[HTML][HTML] Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations
Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …
and modalities using questionnaires, physical signals, and physiological signals. Recently …
Automated emotion recognition: Current trends and future perspectives
Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …
recognition has applications in multiple domains such as health care, e-learning …
Robust speech emotion recognition using CNN+ LSTM based on stochastic fractal search optimization algorithm
One of the main challenges facing the current approaches of speech emotion recognition is
the lack of a dataset large enough to train the currently available deep learning models …
the lack of a dataset large enough to train the currently available deep learning models …
An ensemble 1D-CNN-LSTM-GRU model with data augmentation for speech emotion recognition
Precise recognition of emotion from speech signals aids in enhancing human–computer
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …
Temporal modeling matters: A novel temporal emotional modeling approach for speech emotion recognition
Speech emotion recognition (SER) plays a vital role in improving the interactions between
humans and machines by inferring human emotion and affective states from speech signals …
humans and machines by inferring human emotion and affective states from speech signals …
Speech emotion recognition based on multiple acoustic features and deep convolutional neural network
Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large
number of SER schemes have been anticipated over the last decade. However, the …
number of SER schemes have been anticipated over the last decade. However, the …
Human‐Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks
Emotions play an essential role in human relationships, and many real‐time applications
rely on interpreting the speaker's emotion from their words. Speech emotion recognition …
rely on interpreting the speaker's emotion from their words. Speech emotion recognition …
Graph neural architecture search: A survey
In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …
approach to graph data processing ranging from node classification and link prediction tasks …
Human–computer interaction with a real-time speech emotion recognition with ensembling techniques 1D convolution neural network and attention
Emotions have a crucial function in the mental existence of humans. They are vital for
identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) …
identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) …
Emotional speech recognition using deep neural networks
The expression of emotions in human communication plays a very important role in the
information that needs to be conveyed to the partner. The forms of expression of human …
information that needs to be conveyed to the partner. The forms of expression of human …